langchain/docs/extras/integrations/llms/minimax.ipynb

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   "attachments": {},
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   "source": [
    "# Minimax\n",
    "\n",
    "[Minimax](https://api.minimax.chat) is a Chinese startup that provides natural language processing models for companies and individuals.\n",
    "\n",
    "This example demonstrates using Langchain to interact with Minimax."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup\n",
    "\n",
    "To run this notebook, you'll need a [Minimax account](https://api.minimax.chat), an [API key](https://api.minimax.chat/user-center/basic-information/interface-key), and a [Group ID](https://api.minimax.chat/user-center/basic-information)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single model call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import Minimax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the model\n",
    "minimax = Minimax(minimax_api_key=\"YOUR_API_KEY\", minimax_group_id=\"YOUR_GROUP_ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "# Prompt the model\n",
    "minimax(\"What is the difference between panda and bear?\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chained model calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# get api_key and group_id: https://api.minimax.chat/user-center/basic-information\n",
    "# We need `MINIMAX_API_KEY` and `MINIMAX_GROUP_ID`\n",
    "\n",
    "import os\n",
    "\n",
    "os.environ[\"MINIMAX_API_KEY\"] = \"YOUR_API_KEY\"\n",
    "os.environ[\"MINIMAX_GROUP_ID\"] = \"YOUR_GROUP_ID\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from langchain.llms import Minimax\n",
    "from langchain import PromptTemplate, LLMChain"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's think step by step.\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "llm = Minimax()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "question = \"What NBA team won the Championship in the year Jay Zhou was born?\"\n",
    "\n",
    "llm_chain.run(question)"
   ],
   "metadata": {
    "collapsed": false
   }
  }
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